From 2868fe3df4e92a6ae3e327faf6e57ea492e04124 Mon Sep 17 00:00:00 2001
From: 志浩 <neo.dzh@alibaba-inc.com>
Date: 星期四, 16 三月 2023 19:24:21 +0800
Subject: [PATCH] Merge branch 'main' into dev_dzh
---
egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer.py | 103 +++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 103 insertions(+), 0 deletions(-)
diff --git a/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer.py b/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer.py
new file mode 100755
index 0000000..b3bfe8e
--- /dev/null
+++ b/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer.py
@@ -0,0 +1,103 @@
+import os
+import shutil
+from multiprocessing import Pool
+
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+from funasr.utils.compute_wer import compute_wer
+
+import pdb;
+def modelscope_infer_core(output_dir, split_dir, njob, idx):
+ output_dir_job = os.path.join(output_dir, "output.{}".format(idx))
+ gpu_id = (int(idx) - 1) // njob
+ if "CUDA_VISIBLE_DEVICES" in os.environ.keys():
+ gpu_list = os.environ['CUDA_VISIBLE_DEVICES'].split(",")
+ os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_list[gpu_id])
+ else:
+ os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
+ inference_pipline = pipeline(
+ task=Tasks.auto_speech_recognition,
+ model='NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950',
+ model_revision='v3.0.0',
+ output_dir=output_dir_job,
+ batch_size=1,
+ )
+ audio_in = os.path.join(split_dir, "wav.{}.scp".format(idx))
+ inference_pipline(audio_in=audio_in)
+
+
+def modelscope_infer(params):
+ # prepare for multi-GPU decoding
+ ngpu = params["ngpu"]
+ njob = params["njob"]
+ output_dir = params["output_dir"]
+ if os.path.exists(output_dir):
+ shutil.rmtree(output_dir)
+ os.mkdir(output_dir)
+ split_dir = os.path.join(output_dir, "split")
+ os.mkdir(split_dir)
+ nj = ngpu * njob
+ wav_scp_file = os.path.join(params["data_dir"], "wav.scp")
+ with open(wav_scp_file) as f:
+ lines = f.readlines()
+ num_lines = len(lines)
+ num_job_lines = num_lines // nj
+ start = 0
+ for i in range(nj):
+ end = start + num_job_lines
+ file = os.path.join(split_dir, "wav.{}.scp".format(str(i + 1)))
+ with open(file, "w") as f:
+ if i == nj - 1:
+ f.writelines(lines[start:])
+ else:
+ f.writelines(lines[start:end])
+ start = end
+ p = Pool(nj)
+ for i in range(nj):
+ p.apply_async(modelscope_infer_core,
+ args=(output_dir, split_dir, njob, str(i + 1)))
+ p.close()
+ p.join()
+
+ # combine decoding results
+ best_recog_path = os.path.join(output_dir, "1best_recog")
+ os.mkdir(best_recog_path)
+ files = ["text", "token", "score"]
+ for file in files:
+ with open(os.path.join(best_recog_path, file), "w") as f:
+ for i in range(nj):
+ job_file = os.path.join(output_dir, "output.{}/1best_recog".format(str(i + 1)), file)
+ with open(job_file) as f_job:
+ lines = f_job.readlines()
+ f.writelines(lines)
+
+ # If text exists, compute CER
+ text_in = os.path.join(params["data_dir"], "text")
+ if os.path.exists(text_in):
+ text_proc_file = os.path.join(best_recog_path, "token")
+ text_proc_file2 = os.path.join(best_recog_path, "token_nosep")
+ with open(text_proc_file, 'r') as hyp_reader:
+ with open(text_proc_file2, 'w') as hyp_writer:
+ for line in hyp_reader:
+ new_context = line.strip().replace("src","").replace(" "," ").replace(" "," ").strip()
+ hyp_writer.write(new_context+'\n')
+ text_in2 = os.path.join(best_recog_path, "ref_text_nosep")
+ with open(text_in, 'r') as ref_reader:
+ with open(text_in2, 'w') as ref_writer:
+ for line in ref_reader:
+ new_context = line.strip().replace("src","").replace(" "," ").replace(" "," ").strip()
+ ref_writer.write(new_context+'\n')
+
+
+ compute_wer(text_in, text_proc_file, os.path.join(best_recog_path, "text.sp.cer"))
+ compute_wer(text_in2, text_proc_file2, os.path.join(best_recog_path, "text.nosp.cer"))
+
+
+if __name__ == "__main__":
+ params = {}
+ params["data_dir"] = "./example_data/validation"
+ params["output_dir"] = "./output_dir"
+ params["ngpu"] = 1
+ params["njob"] = 1
+ modelscope_infer(params)
--
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